"""Contains a variant of the densenet model definition."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim


def trunc_normal(stddev): return tf.truncated_normal_initializer(stddev=stddev)

# conv 微结构
def bn_act_conv_drp(current, num_outputs, kernel_size, scope='block'):
    """
    除了第一层外，每一层conv都要加上BN-Relu-Conv-Dropout操作
    :param current: 输入信息
    :param num_outputs: feature map 的channel数
    :param kernel_size: 卷积的size
    :param scope: 命名域
    :return: 
    """
    current = slim.batch_norm(current, scope=scope + '_bn')
    current = tf.nn.relu(current)
    current = slim.conv2d(current, num_outputs, kernel_size, scope=scope + '_conv')
    current = slim.dropout(current, scope=scope + '_dropout')
    return current

def transition_layer(inputs, num_outputs, scope='transition'):
    """
    过渡层--每个block之间加1x1的卷积进行降维
    :param inputs: 输入图像信息
    :param num_outputs: feature map的channel数
    :param scope: 
    :return: 
    """
    network = bn_act_conv_drp(inputs, num_outputs, [1,1], scope=scope+'_conv1x1')
    network = slim.avg_pool2d(network,[2,2],stride=2,scope=scope+'_avgpool')
    # network = slim.conv2d(network,[2,2],stride=2,scope=scope+'_avgpool')
    return network

def block(net, layers, growth, scope='block'):
    for idx in range(layers):
        bottleneck = bn_act_conv_drp(net, 4 * growth, [1, 1],
                                     scope=scope + '_conv1x1' + str(idx))
        tmp = bn_act_conv_drp(bottleneck, growth, [3, 3],
                              scope=scope + '_conv3x3' + str(idx))
        net = tf.concat(axis=3, values=[net, tmp])  # 在第三个维度（深度）进行拼接
    return net


def densenet(images, num_classes=1001, is_training=False,
             dropout_keep_prob=0.8,
             scope='densenet'):
    """Creates a variant of the densenet model.

      images: A batch of `Tensors` of size [batch_size, height, width, channels].
      num_classes: the number of classes in the dataset.
      is_training: specifies whether or not we're currently training the model.
        This variable will determine the behaviour of the dropout layer.
      dropout_keep_prob: the percentage of activation values that are retained.
      prediction_fn: a function to get predictions out of logits.
      scope: Optional variable_scope.

    Returns:
      logits: the pre-softmax activations, a tensor of size
        [batch_size, `num_classes`]
      end_points: a dictionary from components of the network to the corresponding
        activation.
    """
    growth = 24
    compression_rate = 0.5

    def reduce_dim(input_feature):
        return int(int(input_feature.shape[-1]) * compression_rate)

    end_points = {}

    with tf.variable_scope(scope, 'DenseNet', [images, num_classes]):
        with slim.arg_scope(bn_drp_scope(is_training=is_training,
                                         keep_prob=dropout_keep_prob)) as ssc:

            # block 的层数： 6， 12， 24 ，16
            ##########################
            # Put your code here.
            ##########################
            # (-1,224,224,3) --> (-1,112,112,48) 对Densenet-BC,初始的conv层深度为2倍的growth
            network = slim.conv2d(images,2*growth,[7,7],stride=2,padding='SAME',scope='Conv_0')
            # (-1,112,112,48) --> (-1,56,56,48)
            network= slim.max_pool2d(network,[3,3],stride=2,padding='SAME',scope='pool_0')
            # (-1,56,56,48) --> (-1,56,56,192)   192 = 48+ 6*24 层数累加
            network=block(network,6, growth,scope='Block_0')
            # (-1,56,56,192) --> (-1,28,28,96) transition_layer压缩0.5
            network=transition_layer(network,reduce_dim(network),scope='transition_0')
            # (-1,28,28,96) --> (-1,28,28,384)  384 = 96 + 12*24
            network = block(network, 12, growth, scope='Block_1')
            # (-1,28,28,384) --> (-1,14,14,192)
            network = transition_layer(network,reduce_dim(network),scope='transition_1')
            # (-1,14,14,192) --> (-1,14,14,768 )  768=192+24*24
            network = block(network, 24, growth, scope='Block_2')
            # (-1,14,14,768 ) -->(-1,7,7,384 )
            network = transition_layer(network, reduce_dim(network), scope='transition2')
            # (-1, 7, 7, 384)--> (-1,7,7,768 )  768 = 384+16*24
            network = block(network, 16, growth, scope='Block_3')
            network=slim.batch_norm(network,scope='batch_norm_relu')
            network=tf.nn.relu(network)

            # (-1,7,7,768 )-->(-1,1,1,768)
            network=tf.reduce_mean(network,[1,2],keep_dims=True, name='Global_average_pool')
            # (-1,1,1,768) --> (-1,1,1,num_classes)
            network=slim.conv2d(network,num_classes,[1,1],scope='conv_1')

            logits = tf.squeeze(network,[1,2],name='squeeze')

            out = slim.softmax(logits,scope='prediction')
            end_points['logits']=logits
            end_points['predictions']=out

    return logits, end_points


def bn_drp_scope(is_training=True, keep_prob=0.8):
    keep_prob = keep_prob if is_training else 1
    with slim.arg_scope(
        [slim.batch_norm],
            scale=True, is_training=is_training, updates_collections=None):
        with slim.arg_scope(
            [slim.dropout],
                is_training=is_training, keep_prob=keep_prob) as bsc:
            return bsc


def densenet_arg_scope(weight_decay=0.004):
    """Defines the default densenet argument scope.

    Args:
      weight_decay: The weight decay to use for regularizing the model.

    Returns:
      An `arg_scope` to use for the inception v3 model.
    """
    with slim.arg_scope(
        [slim.conv2d],
        weights_initializer=tf.contrib.layers.variance_scaling_initializer(
            factor=2.0, mode='FAN_IN', uniform=False),
        activation_fn=None, biases_initializer=None, padding='same',
            stride=1) as sc:
        return sc


densenet.default_image_size = 224
